Pixel-Superpixel Contrastive Learning and Pseudo-Label Correction for Hyperspectral Image Clustering

TL;DR

This paper introduces PSCPC, a novel hyperspectral image clustering method that combines pixel and superpixel contrastive learning with pseudo-label correction to improve accuracy and efficiency.

Contribution

It proposes a combined contrastive learning approach with pseudo-label correction to leverage both pixel and superpixel features for better HSI clustering.

Findings

PSCPC outperforms existing methods in accuracy.

It reduces computational time compared to pixel-level contrastive learning.

The pseudo-label correction enhances clustering consistency.

Abstract

Hyperspectral image (HSI) clustering is gaining considerable attention owing to recent methods that overcome the inefficiency and misleading results from the absence of supervised information. Contrastive learning methods excel at existing pixel level and super pixel level HSI clustering tasks. The pixel-level contrastive learning method can effectively improve the ability of the model to capture fine features of HSI but requires a large time overhead. The super pixel-level contrastive learning method utilizes the homogeneity of HSI and reduces computing resources; however, it yields rough classification results. To exploit the strengths of both methods, we present a pixel super pixel contrastive learning and pseudo-label correction (PSCPC) method for the HSI clustering. PSCPC can reasonably capture domain-specific and fine-grained features through super pixels and the comparative learning of a small number of pixels within the super pixels. To improve the clustering performance of super pixels, this paper proposes a pseudo-label correction module that aligns the clustering pseudo-labels of pixels and super-pixels. In addition, pixel-level clustering results are used to supervise super pixel-level clustering, improving the generalization ability of the model. Extensive experiments demonstrate the effectiveness and efficiency of PSCPC.